Recruiters receive hundreds of resumes every day. It is difficult and time-consuming to screen them one by one manually. All resumes come in various formats such as PDF, Doc, Docx, etc. that is in unstructured form.
Recruiters receive hundreds of resumes every day. It is difficult and time-consuming to screen them one by one manually. All resumes come in various formats such as PDF, Doc, Docx, etc. that is in unstructured form. These are understood by the human being but not by the system. Automation is the key to reduce the time of resume screening and efforts of recruiters so that they can utilize their time in other productive tasks.
Thus, the resume parser has been introduced to convert the textual content of resume into the structured format so that it can be easily understood by machines. The resume/CV parser must be intelligent enough to parse textual content accurately. The language is hugely varied and ambiguous many times. It means the same word can have different meanings, for example, the abbreviation GM stands for ‘General Manager’ in business but ‘Guidance Manager’ in the military. So, the parser should be intelligent enough to check the difference between both. The more intelligent the resume parser, you will obtain more accurate data.
The parser should be intelligent enough to handle the following tasks:
• It should pick information accurately such as skills, educational details, work history, other academic achievements, etc.
• It should be correctly pick meaning of ambiguous words as per the context. Example, MD can stand for ‘Managing Director,’ ‘Medical Doctor,’ and ‘Maryland.’
• It should identify most relevant job-related skills than adaptive skills such as ‘network engineer’ is more important than ‘efficient’ may you need both for a job profile.
• It should be able to parse personal information accurately.
• It should clearly distinguish the difference between job profile and hobbies otherwise it would end up emailing the wrong candidate.
• It should clearly understand the educational history of the candidate.
• It should distinguish the difference between job skills and secondary skills learned at college or school.
• Semantic search and match: It makes use of synonym matches for skills/competencies, job title, location, education, etc. It generates keyword matches from job/skills aliases and provides recommendations to candidates as well recruiters as required.
• Resume enrichment: It provides candidates’ social details such as experience, social information, job changing behavior, network and company insights. It also updates your old resume database with the latest information.
• Unlimited input options: It supports any format of the resume such as PDF, RTF, DOC, DOCX, HTML, and others.
• Real-time parsing: It parses resumes in real-time and arranges parsed data into segregated fields.
• Connect with any source: It parses resumes or jobs from any source including job boards, LinkedIn.
• Flexible options for output: It provides output in XML or JSON format.
• Data migration: Data migration becomes mandatory for an organization when it changes or upgrades a system. In such a case, resume parser helps to migrate old resume database to the new system to keep the work going.
• Seamless integration of API: Resume parser uses REST or SOAP to make integration easy.
Resume parser is used usually for applicant tracking system (ATS), career sites, job boards, staffing companies, enterprises and other recruitment businesses to automate their resume/CV database.